Sulaiman Rozita, Azeman Nur Hidayah, Mokhtar Mohd Hadri Hafiz, Mobarak Nadhratun Naiim, Abu Bakar Mohd Hafiz, Bakar Ahmad Ashrif A
Photonics Technology Laboratory, Department of Electrical, Electronic, and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Malaysia.
Institute of Microengineering and Nanoelectronics (IMEN), Universiti Kebangsaan Malaysia, Malaysia.
Spectrochim Acta A Mol Biomol Spectrosc. 2024 Jan 5;304:123327. doi: 10.1016/j.saa.2023.123327. Epub 2023 Sep 1.
Accurate, label-free, and rapid methods for measuring phosphorus concentrations are essential in a hydroponic system, as excessive or insufficient phosphorus levels can adversely affect plant growth, human health, and environmental sustainability. In this study, we demonstrate the advantages of hybrid machine learning models compared to single machine learning models in predicting phosphorus concentration based on the absorbance dataset. Three machine learning classifiers- Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)- were employed as bases for single and hybrid machine learning models. Three ensemble techniques (voting, bagging, and stacking) were used to hybridize the classifiers. Among the single models, KNN demonstrated the fastest computational time of 18.07 s, while SVM achieved the highest accuracy of 99.6%. The hybrid SVM/KNN model using a voting classifier showed a significant increase in accuracy for KNN with only a slight increase in computational time. Bagging techniques increased the accuracy but at a longer computational time. The stacking technique, which combined SVM, KNN, and RF, achieved the highest accuracy of 99.73% with a short computational time of 36.18 s compared to the bagging and voting technique. This study demonstrates that the machine learning method can effectively distinguish phosphorus concentrations. In contrast, hybrid machine learning techniques can improve accuracy for predicting phosphorus without using labels, despite requiring longer computational time.
在水培系统中，准确、无标记且快速的磷浓度测量方法至关重要，因为磷含量过高或过低都会对植物生长、人类健康和环境可持续性产生不利影响。在本研究中，我们展示了与单机学习模型相比，混合机器学习模型在基于吸光度数据集预测磷浓度方面的优势。三种机器学习分类器——随机森林（RF）、支持向量机（SVM）和K近邻（KNN）——被用作单机和混合机器学习模型的基础。使用了三种集成技术（投票、装袋和堆叠）来混合分类器。在单机模型中，KNN的计算时间最快，为18.07秒，而SVM的准确率最高，为99.6%。使用投票分类器的混合SVM/KNN模型显示，KNN的准确率显著提高，而计算时间仅略有增加。装袋技术提高了准确率，但计算时间更长。将SVM、KNN和RF结合的堆叠技术实现了最高准确率99.73%，与装袋和投票技术相比，计算时间较短，为36.18秒。本研究表明，机器学习方法可以有效区分磷浓度。相比之下，混合机器学习技术可以在不使用标记的情况下提高预测磷的准确率，尽管需要更长的计算时间。
